Novel opportunities in automated classification of antinuclear antibodies on HEp-2 cells

Rigon, Amelia; Buzzulini, Francesca; Soda, Paolo; Onofri, Leonardo; Arcarese, Luisa; Iannello, Giulio; Afeltra, Antonella

doi:10.1016/j.autrev.2011.04.022

Cited by 44 publications

(24 citation statements)

References 25 publications

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“…It was shown that the segmentation performance of the proposed method achieved 89 % when evaluated based on percent volume overlap (PVO) rate [13,14]. The classification performance using a support vector machine (SVM) [15,16] classifier designed based on the features calculated from the segmented cells achieved an average accuracy of 96.90 %, outperforming the methods proposed in other previous studies [1,[5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 66%

“…The estimated probability of the dominate pattern must exceed a predefined threshold. In this study, [7] 26.00 % 573:37 8-fold a CV Huang et al [4] 7.60 % 1020:n/a 10-fold a CV Voigtet al [10] 6 . 0 0 % n/a:351 b ITT Adopted method [12] 0.31 % 3830:196 5-fold a CV 3.…”

Section: System Verificationmentioning

confidence: 85%

“…Generally, these systems include a module for image segmentation designed based on Otsu thresholding [1][2][3] and watershed segmentation [4], and another module for image recognition built based on various pattern recognition methods [1,[5][6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

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Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases

Cheng

Hsieh

et al. 2015

J Med Syst

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Indirect immunofluorescence technique applied on HEp-2 cell substrates provides the major screening method to detect ANA patterns in the diagnosis of autoimmune diseases. Currently, the ANA patterns are mostly inspected by experienced physicians to identify abnormal cell patterns. The objective of this study is to design a computer-assisted system to automatically detect cell patterns of IIF images for the diagnosis of autoimmune diseases in the clinical setting. The system simulates the functions of modern flow cytometer and provides the diagnostic reports generated by the system to the technicians and physicians through the radar graphs, box-plots, and tables. The experimental results show that, among the IIF images collected from 17 patients, 6 were classified as coarse-speckled, 3 as diffused, 2 as discrete-speckled, 1 as fine-speckled, 2 as nucleolar, and 3 as peripheral patterns, which were consistent with the patterns determined by the physicians. In addition to recognition of cell patterns, the system also provides the function to automatically generate the report for each patient. The time needed for the whole procedure is less than 30 min, which is more efficient than the manual operation of the physician after inspecting the ANA IIF images. Besides, the system can be easily deployed on many desktop and laptop computers. In conclusion, the designed system, containing functions for automatic detection of ANA cell pattern and generation of diagnostic report, is effective and efficient to assist physicians to diagnose patients with autoimmune diseases. The limitations of the current developed system include (1) only a unique cell pattern was considered for the IIF images collected from a patient, and (2) the cells during the process of mitosis were not adopted for cell classification.

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Section: Introductionmentioning

confidence: 66%

Section: System Verificationmentioning

confidence: 85%

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Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases

Cheng

Hsieh

et al. 2015

J Med Syst

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“…This, together with the growing demand for ANA testing, reinforces the need for automation and standardization of IIF evaluation. So far, only a few more or less advanced commercial platforms based on automated motorized camera-microscopes and digital image analysis software have been introduced [16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

The evaluation of cellular immune function in elderly patients with systemic lupus erythematosus

Men

Chen

Zhang

et al. 2019

Korean J Intern Med

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Indirect immunofluorescence (IIF) on human epithelial (HEp-2) cells is considered as the gold standard screening method for the detection of antinuclear autoantibodies (ANA). However, in terms of automation and standardization, it has not been able to keep pace with most other analytical techniques used in diagnostic laboratories. Although there are already some automation solutions for IIF incubation in the market, the automation of result evaluation is still in its infancy. Therefore, the EUROPattern Suite has been developed as a comprehensive automated processing and interpretation system for standardized and efficient ANA detection by HEp-2 cell-based IIF. In this study, the automated pattern recognition was compared to conventional visual interpretation in a total of 351 sera. In the discrimination of positive from negative samples, concordant results between visual and automated evaluation were obtained for 349 sera (99.4%, kappa = 0.984). The system missed out none of the 272 antibody-positive samples and identified 77 out of 79 visually negative samples (analytical sensitivity/specificity: 100%/97.5%). Moreover, 94.0% of all main antibody patterns were recognized correctly by the software. Owing to its performance characteristics, EUROPattern enables fast, objective, and economic IIF ANA analysis and has the potential to reduce intra-and interlaboratory variability.

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“…This necessitates automation of the interpretation of ANA HEp-2 cell images. Several computerbased methods for classification of cell images [12][13][14][15] as well as preprocessing segmentation methods [16] were proposed in the literature, but they have not been benchmarked.…”

Section: Introductionmentioning

confidence: 99%

ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions

Ponomarev

Arlazarov

Gelfand

et al. 2014

Pattern Recognition

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a b s t r a c tThe ANA HEp-2 medical test is a powerful tool in autoimmune disease diagnostics. The last step of this test, the interpretation of immunofluorescent images by trained experts, represents a potential source of errors and could theoretically be replaced by automated methods. Here we present a fully automatic method for recognition of types of immunofluorescent images produced by the ANA HEp-2 medical test. The proposed method makes use of the difference in number, size, shape and localization of cell regions that are targeted by the antinuclear antibodies -the humoral components of immune system that bind human antigens as a result of the immune system malfunction. The method extracts morphological properties of stained cell regions using a combination of thresholding-based and thresholding-less approaches and applies a conventional machine-learning algorithm for image classification.

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Novel opportunities in automated classification of antinuclear antibodies on HEp-2 cells

Cited by 44 publications

References 25 publications

Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases

Design of a Computer-Assisted System to Automatically Detect Cell Types Using ANA IIF Images for the Diagnosis of Autoimmune Diseases

The evaluation of cellular immune function in elderly patients with systemic lupus erythematosus

ANA HEp-2 cells image classification using number, size, shape and localization of targeted cell regions

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